bends in the road can take the car in front out of the beam for a few seconds. So it appears to "remember" the speed of the car in front for perhaps 5 secondsBut the car in front could do an emergency stop in the 5 seconds it's out of view, can the assisted braking respond in the time left after the car reappears?
The more complexity in the control system, the more responsibility on the driver to understand it in detail, and to understand (and take action) when it hits corner cases where it doesn't do what you intend.The more complexity in the control system, the more opaque it generally gets.
if the car in front is slowing to turn a corner, and I can see that it will be safely out of the way before I get close; the radar system (configured for sensitive mode) sees the car in front slowing to a near stop in the line of sight, and rapidly slows down my car. Flicking off the speed control for a second resolves this problem.The problem with automation is fighting it to stop it doing what you don't want can often make more work than doing the job yourself.
The more complexity in the control system, the more opaque it generally gets.If opaque means harder to understand or described in a few sentences, then yes. More complexity often means involving more parameters, which presents more probability for hidden sources of problems.
The problem with automation is fighting it to stop it doing what you don't want can often make more work than doing the job yourself.Solving the problem through automation is only needed to be done once. It's an inevitable direction of technological progress.
Ther are some excellent ideas in the videos but I'm baffled by "load and disturbance" as an input. I guess the terms have some meaning in chemical engineering but I'd appreciate an explanation!After trying other methods, I finally found that the best way to understand a process control system is by splitting it into a process model and a control model. Load and disturbance are inputs for the process model. They affect its outputs.
Also you don't define "set point" anywhere. You and I know what you are talking about, but your student doesn't!A well configured process controller changes its ouput in a way that makes its input (which is fed by a sensor to measure a process value PV) match with controller's set point. In Auto mode, the set point SP is keyed in by an operator. In cascade mode, it's fed by the output of an upstream controller. Output of a controller can be assosiated with control valve opening, frequency of variable speed drive, or duty cycle of a PWM device. It can also be used as the set point of a downstream PID controller in a cascade configuration.
I'm always in favor of beginning at the idiot level. Inputs are what we are given, outputs are what we want, set points specify the parameters of the output......
But what are "load" and "disturbance"? Can you give us examples?Let's take an electric water heater for shower as an example. You already set the desired temperature, e.g. 50 C as the setpoint. If you change the water flow rate, you change the load. Change of weather, e.g. rain/snow pouring down on your water storage tank or pipes may change the supply water temperature, which can be regarded as disturbance.
recognise a BMW badge and make appropriate allowances
https://en.wikipedia.org/wiki/PID_controller#Fundamental_operationHere, the plant/process is treated like a black box. It's true that in many cases, a PID controller can be applied without knowing the details of the process it has to control. Professional system engineers would try to collect information about the real process design as much as possible so they can estimate its characteristics and then set initial parameters required to control it appropriately safer and quicker.
(https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/PID_en.svg/600px-PID_en.svg.png)
A block diagram of a PID controller in a feedback loop. r(t) is the desired process value or setpoint (SP), and y(t) is the measured process value (PV).
In this video we introduce the concept of proportional, integral, derivative (PID) control. PID controllers are perhaps the most popular and widely used control scheme in history. While they are relatively simple, they are surprisingly robust and provide excellent performance in most situations. This video introduces the core concepts in PID controller and sets the stage for various future videos where we will discuss their nuances and details in greater depth.
Topics and timestamps:
0:00 ? Introduction
9:04 ? Proportional control
15:03 ? Integral control
24:49 ? Derivative control
30:41 ? Physical demonstration of PID control
44:16 - Conclusions
PID controller explained! Learn what a PID (Proportional Integral Derivative) controller is and how it works in an easy to follow video.This video introduces PID controller in a more practical way by using a PID simulation software. Unfortunately, it only explore the controller part of the PID system, while the simulated process part is inadequately explained, which makes it hard to get a complete understanding of how PID controllers work. Applying a PID control in real life scenario also needs understanding of limitations and constraints of the hardware, like how the sensors and actuators work, their minimum and maximum working range, saturation points, hysteresis, non-linearity, consider cost of actions, prioritization of control parameters, control structure and control options which might be vendor dependent.
3:48 When proportional gain is too low, the controller output becomes too stable or less reactive, hence it will let the error too big for too long.
⌚Timestamps:
00:00 - Intro
00:49 - Examples
02:21 - PID Controller
03:28 - PLC vs. stand-alone PID controller
03:59 - PID controller parameters
05:29 - Controller tuning
06:20 - Controller tuning methods
=============================
In this video, we?re going to talk about the PID Controller and its transformation from a single station device to what it has evolved into today. We?re going to explain why PID Controllers are used in industrial processes.
We?ll illustrate how Controller settings affect different processes under control. We?ll also provide an overview of Controller Tuning.
Let?s start with a discussion about home temperature control.
If the room temperature is below the setpoint, the furnace is turned ON. When the room temperature increases above the setpoint, the furnace turns OFF.
This type of control is referred to as ON/OFF or Bang-Bang Control. The temperature is not exactly held at the setpoint of 70?F, but cycles above and below the setpoint.
ON/OFF control may be ok for your house, but it is not ok for industrial processes or motion control. Let?s look at an example of tank level control to explain why.
The Valve fills the tank as the pump drains it. If the valve is operated with ON/OFF control, the water will fluctuate around the 50% setpoint. For our purpose, let?s say the fluctuation is ?10%. In most industrial applications, this fluctuation around the setpoint is not acceptable.
What if it?s possible to throttle the valve and place it in any position between ON and OFF?
Let?s look at how a PID Controller fits into a feedback control loop. The Controller is responsible for ensuring that the Process remains as close to the desired value as possible regardless of various disruptions.
The controller compares the Transmitter Process Variable (PV) signal, and the Setpoint.
Let?s refer to the difference between the Process Variable and the Setpoint as the Error signal.
Based on that comparison, the controller produces an output signal to operate the Final Control Element. This PID Controller output is capable of operating the Final Control Element over its entire 100% range.
The PID controller determines how much and how quickly correction is applied by using varying amounts of Proportional, Integral, and Derivative action. Each block contributes a unique signal that is added together to create the controller output signal.
The proportional block creates an output signal proportional to the magnitude of the Error Signal.
Unfortunately, the closer you get to the setpoint, the less it pushes. Eventually, the process just runs continuously close to the setpoint, but not quite there.
The integral block creates an output proportional to the duration and magnitude of the Error Signal. The longer the error and the greater the amount, the larger the integral output.
As long as an Error exists, Integral action will continue.
The derivative block creates an output signal proportional to the rate of change of the error signal. The faster the error changes, the larger the derivative output.
Derivative control looks ahead to see what the error will be in the future and contributes to the controller output accordingly. That brings us to a term called Controller Tuning.
There are many different manual methods for tuning a controller that involves observing the process response after inflicting controller setpoint changes.
One method involves increasing the amount of setpoint change and repeating the procedure until the process enters a state of steady-state oscillation.
Most process controllers, PLC, and DCS loop controllers sold today have Autotuning capability.
The PID controller learns how the process responds to a change in setpoint, and suggested PID settings.
4:40 The steady state error is usually between the initial process value and set point, not beyond it. The integral control is used to remove that error.